The electrocardiogram (EKG) is a commonly monitored vital sign. The QRS complex is the dominant feature of the EKG signal and is used in many clinical instruments such as simple cardiotachometers, arrhythmia monitors, and implantable pacemakers. Under normal conditions, in the absence of muscle artifacts and other electrical noise, a large signal-to-noise ratio prevails and techniques exist for the detection of the QRS complex in particular, R-wave detection. However, in some applications, such as ergonomics, a patient may undergo severe physical stress. The EKG signal will be corrupted with a large non-stationary stochastic muscle artifact signal (EMG) together with extraneous transient and continuous noise components due to electromagnetic interferences. The EMG signal results in color noise being introduced into the EKG signal while 60 Hz electromagnetic interference results in white noise being introduced into the EKG signal. Other electromagnetic interference at higher frequencies will also result in white noise.
In addition, there exists medical techniques which require that the precise start of the cardiac cycle be determined from the EKG signals. One such technique is detailed in the copending application of C. M. P. Kierney, et al. The latter application discloses the utilization of autoregressive analysis techniques for analyzing reflected ultrasonic Doppler shifted signals resulting from the flow of blood cells within internal blood vessels. In Kierney, it is necessary to precisely determine the start and end of each cardiac cycle, so that the Doppler shifted signals for a particular cycle can be divided into a predefined number of time segments so that an autoregressive analysis can be performed on each of these time segments. The start and the end of the cardiac cycles must be precisely determined since the results of the autoregressive analysis are averaged over a plurality of cardiac cycles, and the analysis must be performed at the same relative point in time for each of these cycles.
Therefore, there exists a need for a technique for determining a patient's heart rate from an EKG signal that has various noise artifacts in it. In particular, the need exists for a technique that can reliably and accurately extract the heart rate information from an EKG signal that has been corrupted by both color and white noise.